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Bayesian_neural_network_papers

★ 327 Open GitHub ↗

Papers for Bayesian-NN

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# Bayesian neural network papers

## Orginal
1. [A Practical Bayesian Framework for Backpropagation Networks](https://authors.library.caltech.edu/13793/1/MACnc92b.pdf)

2. [Bayesian learning for neural networks](http://www.csri.utoronto.ca/~radford/ftp/thesis.pdf)

## What I like the most:
1. [Bayesian learning for neural networks](http://www.csri.utoronto.ca/~radford/ftp/thesis.pdf)

2. [Weight Uncertainty in Neural Networks](https://arxiv.org/abs/1505.05424)

3. [Noisy Natural Gradient as Variational Inference](https://arxiv.org/abs/1712.02390)

4. [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://arxiv.org/abs/1506.02142)

5. [VIME: Variational Information Maximizing Exploration](https://arxiv.org/abs/1605.09674)

6. [Functional Variational Bayesian Neural Networks](https://arxiv.org/pdf/1903.05779.pdf)

## Towards better training algorithms
1. [Keeping Neural Networks Simple by Minimizing the Description Length of the Weights](http://www.cs.toronto.edu/~fritz/absps/colt93.pdf)

2. [Practical Variational Inference for Neural Networks](https://papers.nips.cc/paper/4329-practical-variational-inference-for-neural-networks.pdf)

3. [Weight Uncertainty in Neural Networks](https://arxiv.org/abs/1505.05424)

4. [Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks](https://arxiv.org/abs/1502.05336)

5. [Assumed Density Filtering Methods for Learning Bayesian Neural Networks](http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12391/11777)

6. [Dropout Inference in Bayesian Neural Networks with Alpha-divergences](https://arxiv.org/abs/1703.02914)

7. [Deep neural networks as Gaussian Processes](https://arxiv.org/abs/1711.00165)

8. [Noisy Natural Gradient as Variational Inference](https://arxiv.org/abs/1712.02390)

9. [Bayesian Dark Knowledge](https://arxiv.org/abs/1506.04416)

10. [Variational Implicit Processes](https://arxiv.org/abs/1806.02390)

## Towards more expressive posteriors
1. [Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors](https://arxiv.org/abs/1603.04733)

2. [Learning Structured Weight Uncertainty in Bayesian Neural Networks](http://people.ee.duke.edu/~lcarin/PBP_MN_aistats17.pdf)

3. [Multiplicative Normalizing Flows for Variational Bayesian Neural Networks](https://arxiv.org/abs/1703.01961)

4. [Kernel Implicit Variational Inference](https://arxiv.org/abs/1705.10119)

5. [Bayesian Hypernetworks](https://arxiv.org/abs/1710.04759)

6. [Noisy Natural Gradient as Variational Inference](https://arxiv.org/abs/1712.02390)

7. [Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam](https://arxiv.org/abs/1806.04854)

8. [Adversarial Distillation of Bayesian Neural Network Posteriors](https://arxiv.org/abs/1806.10317)

9. [SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient](https://arxiv.org/abs/1811.04504)

## Towards better models (Structure + Prior)
1. [Variational Dropout and the Local Reparameterization Trick](https://arxiv.org/abs/1506.02557)

2. [Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables](https://arxiv.org/abs/1706.08495)

3. [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://arxiv.org/abs/1506.02142)

4. [Variational Dropout Sparsifies Deep Neural Networks](https://arxiv.org/abs/1701.05369)

5. [Bayesian Compression for Deep Learning](https://arxiv.org/abs/1705.08665)

6. [Variance Networks: When Expectation Does Not Meet Your Expectations](https://arxiv.org/pdf/1803.03764.pdf)

7. [Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors](https://arxiv.org/abs/1806.05975)

8. [Bayesian Convolutional Neural Networks](https://arxiv.org/abs/1806.05978)

9. [Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors](https://arxiv.org/abs/1807.09289)

10. [The Description Length of Deep Learning Models](https://arxiv.org/abs/1802.07044)

11. [Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks](https://arxiv.org/abs/1810.03958)

12. [Understanding Priors in Bayesian Neural Networks at the Unit Level](https://arxiv.org/pdf/1810.05193.pdf)

13. [Functional Variational Bayesian Neural Networks](https://arxiv.org/pdf/1903.05779.pdf)

14. [Function Space Particle Optimization for Bayesian Neural Networks](https://arxiv.org/abs/1902.09754)

## Connecting Bayesian neural networks with Gaussian Processes
1. [Bayesian learning for neural networks](http://www.csri.utoronto.ca/~radford/ftp/thesis.pdf)

2. [Gaussian process behaviour in wide deep neural networks](https://arxiv.org/abs/1804.11271)

3. [Practical Learning of Deep Gaussian Processes via Random Fourier Features](https://pdfs.semanticscholar.org/bafa/7e2d586e7bfe77d9a55ac1cff4eb2f6ff292.pdf)

4. [Deep neural networks as Gaussian Processes](https://arxiv.org/abs/1711.00165)

5. [Variational Implicit Processes](https://arxiv.org/abs/1806.02390)

6. [Mapping Gaussian Process Priors to Bayesian Neural Networks](http://bayesiandeeplearning.org/2017/papers/65.pdf)

7. [Functional Variational Bayesian Neural Networks](https://arxiv.org/pdf/1903.05779.pdf)

## Towards applications
1. [VIME: Variational Information Maximizing Exploration](https://arxiv.org/abs/1605.09674)

4. [Bayesian Optimization with Robust Bayesian Neural Networks](https://papers.nips.cc/paper/6117-bayesian-optimization-with-robust-bayesian-neural-networks.pdf)

5. [Bayesian GAN](http://papers.nips.cc/paper/6953-bayesian-gan.pdf)

6. [Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks](https://arxiv.org/abs/1605.07127)

7. [Model Selection in Bayesian Neural Networks via Horseshoe Priors](https://arxiv.org/abs/1705.10388)

8. [Learning Structural Weight Uncertainty for Sequential Decision-Making](https://arxiv.org/pdf/1801.00085.pdf)

9. [Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning](https://arxiv.org/pdf/1803.10123.pdf)

10. [Variational Continual Learning](https://arxiv.org/pdf/1710.10628.pdf)

11. [A scalable laplace approximation for neural networks](https://openreview.net/pdf?id=Skdvd2xAZ)

12. [Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting](https://arxiv.org/abs/1805.07810)

13. [Loss-Calibrated Approximate Inference in Bayesian Neural Networks](https://arxiv.org/abs/1805.03901)

Bayesian_neural_network_papers

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